Functional electrical stimulation involves artificial activation of paralyzed muscles with implanted electrodes and has been used successfully to improve the ability of quadriplegics to perform movements important for daily activities. The range of motor behaviors that can be generated by functional electrical stimulation, however, is limited to a relatively small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial challenge associated with identifying the patterns of muscle stimulation needed to elicit specified movements. To address this limitation, we have developed machine-learning based algorithms that can predict patterns of muscle activity associated with a wide range of complex limb movements. In addition, we have devised a method whereby predicted patterns of muscle activity can then be transformed into stimulus pulse patterns needed to evoke movements in paralyzed limbs. Our goal for this project is to determine whether these approaches, when applied to temporarily paralyzed non-human primates, can be used to produce: 1) a wide range of movements of the hand throughout peri-personal reach space, and 2) configuration of the hand and fingers into a variety of shapes needed to interact with diverse objects in the environment. If successful, this approach would greatly expand the repertoire of motor behaviors available to individuals paralyzed because of spinal cord injury or stroke. Furthermore, this system ultimately might serve as the requisite interface between brain-derived trajectory information and functional electrical stimulation systems needed to realize a self-contained and self- controlled upper limb neuroprosthetic.